Learning the Integral of a Diffusion Model
sander.ai
May 6, 2026
83 min read
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47/100
Summary
Sampling from a diffusion model involves an iterative process where a denoiser estimates the tangent direction to a path through input space. Neural networks can be trained to directly predict the integral that transforms samples from a simple noise distribution into samples from a target distribution.
Key Takeaways
- Flow maps enable faster sampling from diffusion models by predicting any point on a path from any other point on that same path.
- The development of flow maps builds on the principles of diffusion models, which define a bijection between noise and data with unique, non-crossing paths.
- Various sampling algorithms for diffusion models exist, categorized into stochastic and deterministic methods.
- Flow maps facilitate improved sampling steerability and more efficient reward-based learning in addition to faster sampling.